Infused Evolutionary Learning
نویسندگان
چکیده
In evolutionary learning of tasks containing easy and hard instances, the evolved populations tend to be swamped by solutions to the easy instances. This paper proposes infused evolutionary learning as an attempt to prevent the unbalanced treatment of easy and hard instances. In the proposed evolutionary technique initial populations are infused with a good solution to a single hard instance. The technique is evaluated on a box-pushing task, where the results show it to be beneficial to the performance. It is concluded that for box pushing infused evolutionary learning yields better results than conventional evolutionary learning.
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